Continuous GRASP for nonlinearly-constrained global optimization
Maurício G. C. Resende, Ricardo M. A. Silva · 2012
Global optimization seeks a minimum or maximum of a multimodal function over a discrete or continuous domain. In this paper, we propose a continuous GRASP heuristic for finding approximate solutions for bound-constrained continuous global optimization problems subject to nonlinear constraints. Experimental results illustrate its effectiveness on some functions from CEC2006 benchmark (Liang et al. [2006]).